Abstract
Automatic video activity recognition is crucial across numerous domains like surveillance, healthcare, and robotics. However, recognizing human activities from video data becomes challenging when training and test data stem from diverse domains. Domain generalization, adapting to unforeseen domains, is thus essential. This paper focuses on office activity recognition amidst environmental variability. We propose three pre-processing techniques applicable to any video encoder, enhancing robustness against environmental variations. Our study showcases the efficacy of MViT, a leading state-of-the-art video classification model, and other video encoders combined with our techniques, outperforming state-of-the-art domain adaptation methods. Our approach significantly boosts accuracy, precision, recall and F1 score on unseen domains, emphasizing its adaptability in real-world scenarios with diverse video data sources. This method lays a foundation for more reliable video activity recognition systems across heterogeneous data domains.
Abstract (translated)
自动视频活动识别在监控、医疗保健和机器人技术等多个领域至关重要。然而，当训练数据和测试数据来自不同的环境时，从视频数据中识别人类活动变得极具挑战性。因此，在面对未知领域的适应（域泛化）方面显得尤为重要。本文重点关注办公环境中由于环境变化带来的活动识别问题，并提出三种适用于任何视频编码器的预处理技术，以增强其对环境变异的鲁棒性。 我们的研究展示了MViT这一先进的视频分类模型及其与其他视频编码器结合使用我们所提出的技巧时，在面对未见过的数据域时表现出色，超越了现有的领域适应方法。我们的方法显著提高了在未知领域的准确率、精确度、召回率和F1分数，突显其在处理来自不同数据源的真实世界视频场景中的灵活性。 这项研究为建立一个更可靠的跨异构数据域的视频活动识别系统奠定了基础。
URL
https://arxiv.org/abs/2503.12678
